Abstract:

Maps of biophysical and geophysical variables using
Earth Observing System
(EOS) satellite image data are an important component of Earth science.
These maps have a single value derived at every grid cell and standard
techniques are used to visualize them. Current tools fall short,
however, when it is necessary to describe a \emph{distribution} of values
at each grid cell. Distributions may represent a frequency of occurrence
over time, frequency of occurrence from multiple runs of an ensemble
forecast or possible values from an uncertainty model. We identify
these ``distribution data sets" and present a case study to visualize such 2D
distributions. Distribution data sets are different from multivariate
data sets in the sense that the values are for a single variable
instead of multiple variables. Data for this case study consists of
multiple realizations of percent forest cover, generated using a
geostatistical technique that combines ground measurements and
satellite imagery to model uncertainty about forest cover. We present
two general approaches for analyzing and visualizing such data sets.
The first is a pixel-wise analysis of the probability density functions
for the 2D image while the second is an analysis of features identified
within the image. Such pixel-wise and feature-wise views will give
Earth scientists a more complete understanding of distribution data sets.
See
avis.soe.ucsc.edu/nasa_is
for additional information.